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NEW QUESTION: 1
A company has TM1 production servers scattered across the globe. The North American server contains the base data that is used by all servers globally. The source data for the North American TM1 Server resides locally and is updated using an ODBC connection.
What is the optimal TM1 method for keeping the base data in the global TM1 servers consistent with the base data in the North American TM1 Server?
A. Have a compressed copy of the North American production TM1 Server's data directory sent to the other global locations using FTP and restore this data onto each local TM1 Server.
B. Set up a replication and synchronization nightly chore between the TM1 servers across the globe and the TM1 Server in North America.
C. Set up an ODBC connection to the North American production source data on each of the TM1 servers across the globe and run TurboIntegrator processes to update the global TM1 servers.
D. Send copies of the TM1 log files from the North American server to the administrator of each global TM1 server and have them process it against their local TM1 Server.
Answer: B
Explanation:
Explanation/Reference:
Explanation:
NEW QUESTION: 2
あなたの会社は自社運用データをAzureに移行することを計画しています。
どのAzureサービスをデータの保存に使用できるかを推奨する必要があります。ソリューションは以下の要件を満たす必要があります。
安静時にすべてのデータを暗号化します。
会社が生成したキーを使用してデータを暗号化します。
あなたはどの2つの可能なサービスを推薦できますか？正解はそれぞれ完全な解決策を提示します。
注：それぞれ正しい選択は1ポイントの価値があります。
A. Azure Backup
B. Azure Tableのストレージ
C. Azure Blobストレージ
D. Azure Queueストレージ
E. Azureファイル
Answer: C,E
Explanation:
References:
https://docs.microsoft.com/en-us/azure/storage/common/storage-service-encryption-customer-managed-keys
NEW QUESTION: 3
Which of the following is a facial feature identification product that can employ artificial intelligence and can require the system to learn from experience?
A. DSV
B. Digital nervous system.
C. All of the choices.
D. Neural networking
Answer: D
Explanation:
There are facial feature identification products that are on the market that use other technologies or methods to capture one's face. One type of method used is neural networking technology. This type of technology can employ artificial intelligence that requires the system to "learn" from experience. This "learning" experience helps the system to close in on an identification of an individual. Most facial feature identification systems today only allow for two-dimensional frontal images of one's face. Not DSV: Signature biometrics are often referred to dynamic signature verification (DSV) and look at the way we sign our names. [15] The dynamic nature differentiates it from the study of static signatures on paper. Within DSV a number of characteristics can be extracted from the physical signing process. Examples of these behavioral characteristics are the angle of the pen is held, the time taken to sign, velocity and acceleration of the tip of the pen, number of times the pen is lifted from the paper. Despite the fact that the way we sign is mostly learnt during the years it is very hard to forge and replicate.